Policy, Planning, and Research
WORKING PAPERS
Agriculture
Latin America and Carbbean
Country Department II
The World Bank
March 1989
WPS 163
How Infrastructure
and Financial Institutions
Affect Agricultural Output
and Investment
in India
Hans P. Binswanger,
Shahidur R. Khandker,
and
Mark R. Rosenzweig
Prices matter - but so do banks, markets, and infrastructure.
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Plc,Planning, end Research
Agriculture
How do the decisions of farmers, financial Alfter comparing data on these factors, the
institutions, and government agencies interact authors conclude that:
and affect agricultural investment and output in
a region - and to what extent are these "actors" * Agroclimatic factors continue to govern the
influenced by a region's location and agrocli- rate at which districts can take advantage of new
matic endowments (for example, rainfall or the agricultural opportunities, and govem public,
soil's moisture-holding capacity). bank, and private investment decisions.
This paper is an attempt to quantify the * The availability of banks (credit) is more
relationships of key factors, using district-level important than the real interest rate as a factor in
time-series data from India. aggregate crop output and farmers' demand for
fertilizer. Rapid bank expansion in an area
Agricultural opportunities in a district are increased fertilizer demand by about 23 percent,
seen as the joint outcome of the agroclimatic en- rates of investment in pumps 41 percent, in milk
dowments of the district and new technology animals 46 percent, and in draft animals about
that becomes available to it. Better agroclimatic 38 percent. Despite their impact on investment
opportunities improve output (relation 1), but and fertilizer use, the impact of banks on output
also increase the economic return for a private appears to be fairly small (nearly 3 percent).
farm investment - say, in a tractor (relation 2).
Greater private profit in a well-endowed region * Unsurprisingly, commercial banks prefer to
induces farmers to press for more investment in locate in well-watered areas where the risk of
infrastructure (relation 3). Financial institutions drought or flood is relatively low. Bank expani-
find it more profitable to locate where there is sion is facilitated by government investments in
more demand for capital and more repayment roads and regulated markets, which improve
capacity (relation 4) and where good infrastruc- farmers' liquidity and reduce banks' and farm-
ture reduces their costs (relation 5). Private ers' transaction costs.
agricultural investment and use of input is more
profitable the better the agricultural opportuni- * In the 1970s, expansion of regulated
ties (relation 2), the better the infrastructure markets contributed 4 percent to growth of
(relation 6), the cheaper the cost of financial agricultural output and 17 percent to demand for
services (relation 7), and the more favorable fertilizers. Expansion of electrification in-
government's price and interest policies (relation creased output 2 percent in a decade by increas-
8). Exactly the same factors affect the output ing investment levels for pumps and fertilizer.
supply (relations 9, 10, 11). Traditional ap- A primary education added a large 8 percent to
proaches to production function estimate the crop output over the decade, primarily by
direct impact of capital stocks (investment) and increasing fertilizer demand nearly 30 percent.
input use on output (relation 12), ignoring many
of the factors discussed here.
This paper is a product of the Agriculture Operations Division, Latin America and
Caribbean Country Department IT. Copies are available free from the World Bank,
1818 H Street NW, Washington DC 20433. Please contact Josefina Arevalo, room
17-100, extension 30745
The PPR Working Paper Series disseminates the findings of work under way in the Bank's Policy, Planning, and Research
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The findings, interpretations, and conclusions in these papers do not necessarily represent official policy of the Bank.
Produced at the PPR Dissemination Center
THE IMPACT OF INFRASTRUCTURE AND FINANCIAL INSTITUTIONS
ON AGRICULTURAL OUTPUT AND INVESTMENT IN INDIA
by
Hans P. Binswanger, Shahidur R. Khandker,
and Mark R. Rosenzweig
Table of Contents
I. Introduction ....................................... 1
It. Analytical Framework. 3
III. Data and Variables ................................. 11
IV. Agroclimatic Endowments, Infrastructure,
Population and Crop Output ......................... 17
V. Development of Commercial Banks .................... 22
VI. Determinants of Private Investment ................. 24
VII. Determinants of Fertilizer Demand and Aggregate
Crop Output ........................................ 31
VIII. Discussion ......................................... 35
References ............................................... 42
I. Introduction
Government expenditure on physical infrastructure and human
resource development influence the private production and investment
decisions in agriculture and thus are essential ingredients of increased
agricultural productivity. Government investments (both physical and
human) can directly increase agricultural output by shifting the production
frontier as in the case of irrigation. This is what might be called the
direct effect of government infrastructure. Government investment also
increases the rate of return to private agricultural investment and thereby
leads to greater investient and output. Moreover, by increasing the
viability and profitability of financial intermediaries,.infrastructure can
facilitate the emergence and growth of financial institutions that increase
access to working and investment capital or reduce the costs of borrowing
for long-term investment. Better credit facilities, by enab}ing the
smoothing of consumption, may also increase the willingness of farmers to
take risk.
11 This paper would not have been feasible without the patient and
persistent efforts of a large number of people who assembled, checked
and processed the enormous data base required. The ICRISAT economics
staff graciously provided their district data base for the semiarid
tropics. Robert Evenson and Ann Judd in turn contributed their North
India district data base. James Barbieri assembled the data required
to update these data bases to 1982, added additional states, and
collected the data on banking, on private capital stocks and
investment. He was advised in this endeavor by Devendra B. Gupta who
provided the liaison with the respective Indian authorities who were
gracious to release data which were still in manuscript form. Dr. D.R.
Gadgil opened the data base of NABARD and personally organized the
Continued on next page
-2-
Thus agricultural output and investment respond to the separate
actions undertaken by three economic actors-- faxsers, government agencies
and banks. All three actors respond positively to agricultural
opportunities implied in the agroclimatic endowments of a region and in new
technologies when it becomes available. The magnitude of the effects
depends on how each of the actors responds to agricultuzal opportunities
and how farmers and banks respond to the government investment decisions.
This paper seeks to quantify the inter-relationships among the investment
decisions of governmeat, financiaI institutions and farmers and their
effects on agricultural investment and output.
The central problem of estimating these relationships is that,
once government agencies and banks are admitted as actors who respond to
agricultural opportunities,. one can no longer take the distribution of
government infrastructure and banking iastitutions as exogenously given or
randomly distributed. The impact of government infrastructure on
investment and output is likely to be more pronounced in a better endowed
region than in a poor endowed region and governments will therefore invest
more where opportunities are greater. The resulting unobserved variable
problem may be circumvented by either a precise quantitative
characterisation of the agroclimatic potential or by using the fixed
effects technique of estimation. For analyzing growth in aggregate output
Continued from previous page
assembly of the banking data. Apparao Katikineni organized the
screening, and further computer processing of the many different and
disparate data bases which had to be merged into a single data base.
Kathy Graham did all of the cartographic measurements. We were also
assisted by Sneylata Gupta and Dan Ghura.
-3-
both Binswanger, Yang, Bowers and Mundlak (BYBM, 1986) and Lau and
Yotopoulos (1988) have implemented the appropriate fixed effects technique
using time-series x cross section data of countries.2
The paper is structured in the following order. Section II
outlines an analytical framework. Section III describes the data and
discusses the variables. Section IV shows how various agroclimate
variables affect government decision on where to build roads, markets and
schools and where to provide electricity and canal irrigation. Section V
looks at the joint impact of agroclimatic endowments and infrastructure on
the growth of financial intermediaries. We do this in a cross-section and.
time series framework using district level data from India. Section VI
then considers the joint determination of private agricultural investment
as functions of agroclimatic endowments, technology change, infrastructure,
financial intermediaries and of price and interest policy. In Section VII
we estimate the impact of the same variables on fertilizer demand and
aggregate crop output. The results' are summarized in the concluding part
of the paper.
II. Analytical Framework
In our framework (see Figure 1), agricultural opportunities in a
district are the joint outcome of the agroclimatic endowments of the
district and the new technology which becomes available to the district
2/ Bapna, Binswanger and Quizon (BBQ, 1984) used random effects techniques
to analyze output supply in time-series x cross section data of
districts in India.
AGRICULTURAL
OPPORTUNITIES 0 PRIVATE
- AGROCLIMATIC agroclimate effects () INVESTMENTS
ENDOWMENTS AND INPUT USE
- NEW TECHNOLOGY financial
\ ~~~~~~~syste ;f f t /-
prod. policy effects
grntr) function
pressure
groups ~~~~~~~~~~~~~~~~~~PRICE AND
| ~~~~~~~~~FINANCIAL direct effects /INTEREST
< t>~ ~ ~~) INSTITUTIONS /.POLICY
effeo lcts effects
infrastructure effects (IQ|
Figure 1: Major Relationships among Agroclimte Radowents,
?1nanclal Institutions, Government Infrastructure and
Agrliltural Investment and Output
-5-
from industry and from foreign, national and state research systems.3 T:^e
same technology is potentially available to all districts., but the exten:
to which it is actually applicable to a given district depends on its
agroclimatic endowments. For exampli high yielding varieties of wheat are
not relevant for districts with high wdnter temperatures or districts with
excessive amounts of rainfall and flooding problems. Thus the size and the
growth of the set of agricultural opportunities varies across a district
according tu their agroclimatic characteristics.
Better agroclimatic opportunities such as better rainfall, a
higher moisture holding capacity of the soil and a better irrigation
potential directly affect agricultural output (relation 1). But better
opportunities also increase the economic return to private farm investments
such as tractors; draft animals or pumpsets (relation 2). The greater
private profitability of agriculture in well endowed regions induces
farmers to press government for increased investment in the supportive
infrastructure (relation 3). Financial institutions find it more
profitable to locate in environments where a good agroclimate and rapid
technical change leads to a substantial demand for agricultural investment
and-working capital and a high repayment capacity (relation 4) and where
good infrastructure reduces their cost of intermediation (relation 5).
Private agricultural investment and input use is more profitable the better
the agricultural opportunities (relation 2), the better the government
3/ Although technology investments are Lhemselves government decision
variables, for the purpose of this paper technology is treated as
exogenous to the decisionmaking of specific-government agencies, banks
and farmers when they make investment, location and input decisions.
-6-
infrastructure (relation 6), the cheaper the cost of financial services
(rel&tion 7) and the more favorable price and interest policies are which
are pursued by the government (relation 8). Exactly the same factors
affect the output supply. (relation 9, 10, 11). This means that
agricultural opportunities must be translated into public and private
investment 'fforts in order to affect agricultural output (For a discussion
see BYBM, 1986 and Mundlak, 1985).
The traditional production function approach has attempted to
estimate the direct impact of capital stocks (investment) and input use on
output, (relation 12), ignoring much of the factors discussed here and all
the simultaneity problems (see for example Hayami and Ruttan, 1986).4
Estimation Equations and Econometric Specifications
Let Rrjt be the level of the r-th infrastructure variable (say.
roads) in district j at time t. As agroclimate variables ar-e strictly
exogenous, the dependence of the infrastructure variables on a set of
measured agroclimate variables and location factors ai can be estimated
simply in a cross section regression -
(1) Rrjt Rrjt (0j, Ppj ejt)
4/ A profit-oriented producer will take input and output decisions
jointly. In order to deal with this simultaneity, the correct way to
estimate the production function (12) is to use the predicted instead
of actual levels of capital stocks and other inputs. However, as
clearly apparent from figure 1, we do not have any instruments which
affect only the inputs but not the output. -Therefore the production
function cannot be estimated econometrically.
-7-
where p3 is the effect of unmeasured agroclimatic and location factors, and
ejt is a time-specific error term. 5
Financial institutions in turn are assumed to locate in districts
with good agroclimate and infrastructure, i.e.
(2) Bjt - Bjt (Rjt, aj, jj, Tjt, ejt)
where Bjt stands for the number of banks operating in the district at time
t, Tjt is a region-and time-specific technology index, and ejt is an error
term specific to the banking equation.
The simultaneity between banks, Bjt, and infrastructure, Rjt,
arising from their joint dependence on unobserved agroclimatic variables,
, can be overcome if an additive model is chosen such as
(3) Bjt aO +aj Rjt + a2 Uj +a3 IJ + Tjt + ejt
For the mean overtime in district j this relationship reads
(4) Bj. 3 ao + al Rj. + a2aj +a3 /pj + Tj. + ej.
Taking the difference of these equations, i.e. by transforming the
variables to differences from their means, leads to the following
estimation equation in terms of difference from the means
(5) (Bjt - Bj.) - al (Rjt - Rj.) + (Tjt - Tj.) + (Ejt - ej.)
5/ Similar ultimate reduced forms can be established for all other
endogenous variables in the system, the banks, private investment and
output. However, the reduced forms for output, for example, is not
very informative as it includes both the direct or technical impact of
the agroclimate on output (relation (1)) as well as all the indirect
effects via its impact on infrastructure, banks and private investment.
As et is a randomly distributed error torm which is uncorrelated
with oj and Rjt, relationship (5) can be estimated by the Ordinary Least
Squares technique.
The disadvantages of using this fixed effects model is that the
direct impact of the agroclimate (i.e. relation (4)) cannot be estimated. 6
These direct effects could only be estimated (in this and all subsequent
equations) if the infrastructure variables were randomly distributed acrc-s
the districts, i.e. were not dependent on the unobserved agroclimatic
variables pi. This could happen if the measurement of the observed
agroclimatic variables oj were so good that no unobserved effects were left
over to significantly affect the infrastructure investments. In that case
a random effects model would be appropriate. We will use Hausman-Wu
specification tests to determine whether to use the fixed effects or random
effects model and present results accordingly.
Private agricultural investment in capital item k (say draft
animals or tractors), depends on the agroclimate, the infrastructure and
the banks. In addition it depends on policy variables such as the output
price P, the fertilizer price Pf, and the real interest rate r, i.e.
(6) lkjt - Ikjt (Kjt-I, Pf3jt' jt' B3jt Rjt, Tjt, 'jp Pjt ejt)
where Kjt-l is the capital stock of year (t-1). In these equations the
6/ As discussed in footnote 1, it cannot be estimated either in a reduced
form equation of the form of equation (1).
-9-
simultaneity problem between Bjt, Rjt, and p can be overcome just as for
equation (3) by using the fixed effects technique. For the interest rate,
however, a simultaneous equation problem may arise if higher investments
demand leads credit s npliers to raise the interest rate. In the
application .below this problem is minimized by the fact that the interest
rate used is fixed by the government of India. Only if the government
specifically takes agricultural investment demand (rather than more
aggregate credit conditions) into account in fixing the interest rate will
the simultaneity problem persist. For the other prices a simultaneity can
also arise if increased investment leads to higher output supply (and
fertilizer demand) and therefore lower output prices (higher fertilizer
prices). The fertilizer price used below As the railhead price set by the
government and the endogeneity problem is likely to be minimal. However,
in order to overcome the simultaneity problem associated with output prices
we use an index of international commodity prices as an instrumental
variable for the domestic price index. *As India is a small country in
virtually all international agricultural commodity markets, this completely
eliminates the simultaneity bias.7 The index of international commodity
prices is computed separately for each district using district-specific
production weights for the year 1975. Since agricultural wages are clearly
endogenously determined with agricultural output and investment we replace
this variable with state-specific urban wages as an instrument.
7! India has state trade in agriculture. So the domestic prices do not
correspond in any simple way to international prices. Nevertheless,
estimates show that domestic prices respond positively to international
prices, with a lag of 3 to 4 years.
- 10 -
As can be seen from figure 1, the variables entering the aggregate
crop supply equations are exactly the same as those entering the investmen:
equation and all estimation problems are the same.
Obviously no data exist on the district-specific index of
available (but not necessarily implemented) technology Tjt. Evenson and
Kislev (1975), BYBM (1986) and others used expenditure data, or manpower
data for public research and for extension. (Alternatively Boyce and
Evenson (1975) has used research publications). However, data for these
variables do not Wxist at the district level. Moreover they do not include
technology opportunities arising from private industry and seed
corporations. Other researchers have used simple time trends to proxy
technology opportunities (e.g. Binswanger, 1974). However, simple time
trends assume technology opportunities grow at constant and identical rates
in each district. But the point is that agroclimatic endowments affect the
extent to which new technology option; are applicable to a district. Hence
technology trends must differ across districts. If, for example, banks
systematically located in districts where the green revolution had raised
the input and borrowing requirements of farmers, an output supply function
including common time trends and banks would erroneously allocate to banks
a part of the output contribution of technology, i.e. the coefficient of
banks would be upwards biased. In order to circumvent this problem the
district specific technology trend is entered into the models as follows:
(7) Tjt - Ebm amj t + btt
m
The regression include a common time trend and interaction terms
of all the agroclimatic variables with the time trend, and the district
- .11 -
specific time trend is an estimated function of time and time x climate
interactions. 8 For example in the output supply equation one would
expect the interaction between time and irrigation potential to be
positive, consistent because high yielding varieties of rice and wheat
require good control over water and soil moisture.
III. Data and Variables
The data we have used here are drawn from India. The cross-
section units are 85 randomly drawn districts of India. These 85 districts
belong to 13 states of India--Andhra Pradesh, Bihar, Gujarat, Haryana,
Jammu and Kashmir, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Punjab,
Rajasthan, Tamil Nadu and Uttar Pradesh. The 85 districts are part of the
99 districts from 17 states randomly drawn by the India's National Council
for Applied Economics for its well known Additional Rural Income Survey.9
The period covered in this paper is the agricultural years 1960/61 to
1981/82. The study period covers the agricultural years 1960/61 to
1981/82, but for some dependent variables with more limited data
availability shorter periods are used.
8/ Alternatively one could introduce a trend for each district separately
as in Lau and Yotopoulos.
9/ In fact, 80 districts are drawn from NCAER sample, while the remaining
5 districts are the ICRISAT districts. All NCAER districts could not
be included because of deficiencies in the data for both primary and
secondary districts.
- 12 -
TABLE 1: Descriptive Statistics of Variables
Variable Number of Mean Standard
Observations Deviation
Dependent variables
Aggregate crop output index 1785 1.192 1.045
Fertilizer consumption,
nutrient tons/10 sq. km 1148 22.054 924.045
Net investment in draft animals;
numbers per year/10 Sq.Km 304 4.962 15.504
Net investment in tractors;
numbers per yearllO Sq.Km 304 0.119 0.260
Net investment in pumps; numbers
per yearilO Sq.Km 304 1.343 1.864
Net investment in milk animals;
numbers per year/10 Sq.Km 304 13.555 25.489
Net investment in small stocks;
numbers per year/10 Sq.Km .304 5.308 15.291
Rural population, numbers/10 Sq.Km 1785 2070.304 1547.327
Time-varyina independent variables
Canal irrigation, '000 ha/10 Sq.Km 1785 0.064 0.099
Number of villages with primary
schools/10 sq. km 1785 1.139 0.605
Electrified villages, numbers/10 Sq.Km 1785 0.688 0.764
Commercial banks, numbers/10 Sq. Km 1785 0.069 0.108
Regulated niarkets, numbers/10 Sq.Km 1785 0.014 0.022
Road length, '000 km/10 Sq.Km 1785 4.389 4.277
Real interest rate of cooperative
societies 1785 4.010 4.485
Aggregate real domestic crop
- price index 1785 0.968 0.295
Aggregate Real International
Price index 1785 0.687 0.355
Real urban wage (annual earnings) 1785 A186.336 1195.586
Real fertilizer price index 1785 3.413 0.505
Annual rainfall (in mm) 1785 1138.573 986.503
Time-invariant independent variables
Length of rainy season
in months 85 3.653 1.368
Number of excess rainy months 85 1.236 1.393
Number of cool months
(Temp < 180) 85 0.935 1.313
Percentage of district area
liable to flooding 85 1.389 3.531
Irrigation potential, percentage 85 30.001 31.897
Urban distance (km) 85 298.441 152.029
Soil moisture capacity index 85 2.350 1.009
- 13 -
For each district the aggregate crop output is the index of 20
major crops with district-specific prices of 1975/76 as the base. -
Agricultural investment is represented by private investment in draft
animals, milk animals, small stock (i.e. sheep and goats), tractor, and
irrigation pump. Investment for each period is the difference in the
stocks meaiured for each of the agricultural censuses which occurred at
five year or six year intervals. It is therefore net investment per year
during each of four intercensal intervals. Government infrastructure
consists of primary schools, canal irrigation, rural electrification,
regulated rural markets, and total road le.ngth. The only rural financ.al
intermediary is the number of rural branches of Commercial Banks--the only
-comparable data available for the study period 1960-81. The variables that
characterize the district's agroclimate environment are the irrigation
potential, the length of the rainy season in months, the number of months
in a year with excessive rain (where rainfail exceeds potential
evapotranspiration),l0 the number of cool months in a year when mean
temperature is less than 18 degree Farenheit (this is related to the
ability to grow wheat), the proportion of a district liable to flooding, an
index of the moisture capacity of the soils in the district, and the
district's distance to the nearest major urban center (out of eight centers
i.e., Delhi, Bombey, Madras, Banglore, Kanpur, Ahmedabad, Hyderabad and
Calcutta). The price variables consist of the annual per capita earnings
of industrial workers as a proxy for urban wages, a price index of
fertilizers at railheads, the rate of interest charged by the Primary
Agricultural Cooperative Societies, and the district specific real
10/ Evapotranspiration is a sum of transpiration via plants and evaporation
from soil.
- 14 _
international price index for the aggregate output. The international
prices of 17 crops have been converted to Rupees at the official exchange
rate and aggregated using district-specific production weights for the
agricultural year 1975/76. Domestic price indices using district level
farm harvest prices have also been computed for comparison. All the prices
or price indices are deflated by the consuner price index for rural workers
using 1975/76 as the base year. The interest rate charged by cooperative
credit, societies is the expected real rate i.e., the nominal rate for the
current year less the average percentage increase in the consumer price
index for the previous five year, i.e. inflationary expectations are
assumed to form over a five-year period.11 The means and standard
deviations of these variables are listed in table 1.
The data relating to the agroclimate and the urban distance are
single data points for each of the 85 district, because they are the
permanent characteristics of each district. The data relating to
agricultural output, government investment, prices and Commercial Banks are
time-series data covering the period, 1960/61-1981/82. In contrast, the
investment data refer to the four intercensal periods 1961-66, 1966-72,
1972-77, 1977-82. When investment equations are estimated the independent
variables are their respective means over the corresponding census
intervals.
11/ In both the output supply and investment equations, the inflation rates
are averaged over five years, except for the years prior to 1965 where
data limitation led us to use averages for 2, 3 and 4 years
respectively. The rate of inflation did not fluctuate sharply in the
late fifties, or early sixties.
- 15 -
The following variables require additional explanation: The
azzregate output index reflect both variation over time in each district
relative to its base year 1975 as well as variations in output across
districts relative to a hypothetical average district formed by computing
the averages of all variables across districts in 1975. The agregate
output is normalized for district size, i.e. it c-mpares aggregate "yields"
per unit of geographic area. When fixed effect techniques are used the
across- di-trict variability is of course lost.
'Rezulated markets do not include all rural markets but only those
where government provides market infrastructure and regulates all trades
via a supervised auction system. The government does not regulate the
market price but may enter as a purchaser in order to prevent market prices
from falling below its guaranteed level.
The rural banking system is complex. It consist of traditional
moneylenders and traders for whom no data exist. Cooperative credit
societies were the first formal institutions to achieve wide rural
coverage. They lend largely for short-term production purposes such as
fertilizers. By 1969 such societies existed in virtually all districts of
India covering 94 percent of the villages in the country and they were
providing 5i percent of total formal credit extended to famers. Their
number has been declining as smaller societies have been merged in recent
years. Yet by 1979 they were providing 49 percent of all formal credit.
We include the regulated government lending rate of these societies among
the explanatory variables. At this regulated interest rate credit
rationing is pervasive and we test whether the rate has an influence on
- 16 -
output and investment despite the rationing. Land Development Banks are
also cooperative institutions which lend primarily for investment purposes.
Between 1969 and 1979 their share of lending in formal credit increased
from 15 percent to 19 percent. Their lending rates are closely tied to the
official rates of the Cooperative Credic Societies, i.e. the interest
variable will capture the effect of interest rate changes of both the
cooperative credit societies and the Land Development Banks.
The Commercial Banks, in 1960, were not involved in lending to
farmers except perhaps to plantations and very wealthy farmers. However,
they did considerable lending to agroindustrial enterprises. After their
nate 'alization in 1969 they were compelled by the government to expand
their lending to farmers and agroindustry with targets set both for number
of rural branches as well as the proportion of lending to the agricultural
sector. Between 1969 and 1979 the share of commercial banks in formal
credit provided to farmers rose from 34 percent to 49 percent at the all
India level. In the 85 districts considered here the role of commercial
banks rose even farther. Their share in formal credit rose from 52 percent
in 1972 to 72 percent in 1.979. At the same time the number of rural and
semi-urban commercial bank branches rose from 3,625 to 7,690. Unlike the
volume of lending and outstanding loans in any period, which is influenced
by farmer demand, the number of branches is strictly controlled by the
Banks and'therefore strictly exogenous to farmer decision making. Other
than for the joint dependence of farmer and bank decision making on
agroclimatic and infrastructure variables, no endogeneity problem therefore
arises. Unfortunately the number of Cooperative Societies and Land
Development Banks cannot be used in these equations as exogenous measure of
the growth of these systems because the process of consolidation has
- 17 -
reduced their numbers but not necessarily the availability of their
services in the affected villages.
Soil moisture capacity is a variable which can be viewed as a
substitute for either rainfall or irrigation. For a given rainfall a
higher soil moisture capacity means that a crop can withstand more days
without additional rainfall. In addition where soil moisture capacity is
yery high, a full moisture reservoir in the soil may be able to support
several months of a crop cycle without the addition of rainfall or
.irrigation. FQr given annual rainfall, payoffs to irrigation investments
ara therefore more limited where soil moisture capacity is higher.
Irritation potential is defined as the percentage of a district's
area inside any type of irrigation command area, i.e.. sum of proposed.
command area, command area under construction and already existing command
area.12 This-variable has been measured using the Irrigation Atlas of
India. Planned command areas are a good indicator o. the remaining
potential for canal irrigation in India as they reflect long range plans
and any area not yet included in these plans has virtually no potential.
IV. Azroclimatic Endowments. Infrastructure. Population and Crop Output
In Table 2 we see that the seven measured agroclimatic and
location factors explain between 24 percent of the variation in the density
of primary schools to 41 percent of the variation in government provided
12/ An irrigation command area is an area whilc receives or is expected to
receive water from an irrigation system.
- 18 -
Table 2: Effects of Aprocilmat. Endowment. on Infrastructure. Population
and Asaresate Crop Output
(Observation a 56)
Explanatory Rural Regulated Canal Primary Rural Aggregate
Variables Road Morket Irrication School Electricity Population Crop Output
Cool months -0.222 0.004 0.912 0.303 0.389 285.725 6.229
(-0.587) (1.647)e (1.559) (4.770). (4.293)o (2.261)o (2.417)*
Excoss rain 0.229 -4.O02 -0.M1 -0.041 0.009 42S.138 0.019
(0.477) (-0.761) (-4.667) (0.077) (2.679)* (2.679). (O.168)
Rainy seson 2.442 -4.064 9.oo7 0.079 0.089 S41.898 0.264
(4.901)e.(-0.140) (0.708) (0.988) (6.775) . (3.86). (2.261)*
Flood potential -0.077 -O.01 -O .W14 0.021 -0.064 -10.649 -0.694
(-4.S09) (-1.622). (-1.234) (9.862)- (-1.826)o (-0.226) (-2.574).
Irrigation 0.033 0.001 0.002 0.003 6.W69 20.589 0.034
potential (1.93S). (5.872)o (6.706)o (1.127) (2.332). (3.712). (8.262).
Soil moisture 0.422 0.004 -0.619 0.116 0.231 -14e.635 0.102
capacity (1.676). (2.827). (-2.187). (2.159)* (-0.982) (-8.034) (0.914)
Urban distance -4.003 0.0W04 0.6001 4.0002 -0.001 -0.671 0.034
(-9.118) (2.881)- (2.266). (-0.320) (-1.419) (-0.825) (0.026)
Constant -4.218 -..014 -0.018 0.586 0.186 -416.025 -0.804
(-1.692). (-9.97) (-9.384) (1.474) (0;826) (-4.524) (-1.852)
Adjusted R-cquarr 0.87 6.8 0.48 0.24 9.28 0.41 0.53
Not.s: t-Statisticu are in parentheses. Asterisk refers to significance level ot 10 percent or
better. Rural road corrosponds to the agricultural year 1974, while the remaining
variables relate to agricultural year 1981.
- 19 -
irrigation and the population density of the region. The explanatory
power is thus very substantial, not much smaller than that for output
itself (53 percent). The traditional treatment of agricultural
infrastructure as exogenous variables in output supply analysis is
unwarranted.
The variable with the most powerful effect across the equations is
irrigatiorn potential, i.e. the proportion of the area which is included in
an existing or planned irrigation command area. It significantly increases
the density of all infrastructure variables, except schools. It is also
clear that population has migrated to, or grown more rapidly in regions
with a high irrigation potential, i.e. private and public decisions are
influenced by the same factors.
For the other variables the impact varies substantially across the
government investments. Regions with a fairly cool winter, which are able
tRo grow wheat, have higher density of regulated markets, more primary
schools, more electrification and higher population density. Population
density is also very high in regions with many monthn with excess rain,
i.e. the humid tropical zones such as Kerala. Population and roads are
higher the longer the rainy season, or said otherwise, government has found
it less worthwhile .to build roads in semiarid and arid regions. Areas
which are liable to flooding are not well served by regulated markets,
roads, and electrification. As discussed in the data section, the results
show that high soil moisture capacity acts as a substitute for canal
irrigation. But high soil moisture capacity is a positive agroclimatic
attribute and thus attracts investment in regulated markets, roads and in
electrification. Distance to major urban centers tends to increase the
- 20 -
Distance to major urban centers sends to increase the density of regulated
rural markets, and also the level of government provided irrigation.
One point which stands out from the regressions is that for the
purpose of government infrastructure investments, agroclimatic potential
cannot be measured by a single variable or an agroclimatic index.
Different aspects of the endowment affect the govrernment investments
differentially.
The total effects--both direct and indirect effects via government
infrastructure and banks--of agroclimate and location factors on output
supply are also shown in table 2. They suggest that agroclimatic
endowments explain 53 percent of the variation in agricultural production
of 85 districts in the agricultural year, 1981. Agricultural output supply
is high in regions well endowed with water from either irrigation or
rainfall. Regions with a relatively cool winter have high agricuitural
output while agricultural output is low in regions with a high flood
potential. All the effects are as-expected.
Using data for the three-census years (1961, 1910, 1981) we
investigate in table 3 whether the investment trends over the past two
decades were similiarly influenced by the agroclimatic characteristics.
This is done in regressions which include time trends according to equation-
7, i.e. interactions between time and the agroclimatic characteristics.
The Hausman-Wu test suggests that a fixed effects model is appropriate for
explaining variation in public infrastructure ovdr time, while a random
effects model can be used for rural population growth.
- 21 -
TABLE 3. Effects if Agroclimate Endowments on Growth in Infrastructure and Population.
(Obe. a 265)
Explanatory Regulated Canal Primary Rural Rural
Variable Market Irrication School Electrification Population
Fixod Effeets Fixed Effects Fixed Effects Fixed Effeets Random Effects
Yoor 0.0006 0.0001 0.008 -0.004 0.283
(1.614) (0.162) (1.623) (-0.222) (t.034)
Yoor A cool months 0.0001 -0.000 6.010 0.018 4.643
(1.624). (-0.237). (7.632)* (5.329). (3.229).
Year x excess rain -0.000 0.0005 -0.001 -0.001 6.186
(-0.283) (3.269)* (-0.526) (-0.305) (3.640)*
Yoar x flood -0.000 0.0001 0.0002 -0.002 -0.163
potential (-2.063)o (1.149) (0.411) (-1.042). (-4.361)
Year a lrrigation O.WO 0.60 00 0.66 6.0104 0.235
potential (5.683)0 (2.152)* (-0.456) (2.489). (3.757).
Year x soil 0.6of -e.00o 0.005 0.011 -1.847
moisture capacity (0.184) (-0.126) (3.132)o (2.801). (-1.0S5)
No. of cool months -96.S23
(0.648)
Excess rain -79.314
(40.431)
Rainy season 121.471
(6.644)
Flood potential 0.2S3
(0.004)
Irrigatton potential 1.835
(0.283)
Soil moisture -1.406
capacity (0.008)
Urban dish, g --S.494
(0.534)
Constant -494.96C
(-0.852)
F-Statiptic 32.152 C.148 63.427 38.991 56.113
Hausen-Wu
(Chi-square,7) 16.222 19.274 20.482 22.326 1.364
Note: Asterisk refers to sIgnifIcance level of 10 percent or better on * two-tall teat.
- 22 -
The results are consistent with the simple cross section results.
They suggest that better agroclimatic attributes such as irrigation
potent!al contribute to the growth in public infrastructure as well as
population, while unfavorable attributes such as flood potential reduces
their growth over time. This clearly indicates that agroclimatic
endowments did not only affect the placement of public programs and
institutions in the distant past, but also their growth over the study
period.
V. Development of Commercial Banks
In Table 4 the cross-section results indicate that Commercial
Banks have tended to locate in areas which are well endowed with water,
either from irrigation or from a long and over-abundant rainy season. Such
areas are characterized by relatively low risk of agriculture and therefore
less repayment'problems for the banks (Binswanger and Rosenzweig, 1986).
Th. implies that the banks have avoided areas where drought risk is high.
That banks try to avoid'high-risk areas, is also apparent in the negative
coefficient of flooding potential.
The simple cross section relation for the year 1980 included the
indirect effects of the agroclimate on Bank, via the improved
infrastructure. The pure infrastructure effects, on the other hand, are
estimated and peesented in Table 4 using the cross-section time-series data
of 85 districts for the years 1972-80. As the results of the Hausman-Wu
test suggest, the fixed effects model appears more appropriate than the
random effects model in explaining the variation in the bank growth over
time and only the fixed effects results are therefore shown. The
-23 -
TABLE 4: Effects of Azcoclimatic Endowments and
Government Infrastructure on Commercial Bank
Cross-section effects Fixed effects
Explanatory Variable (observations * 85) (observations - 765)
Year 1980
Canal irrigationa -0.193
(-2.190)*
Regulated Marketa 0.196
(3.227)*
Primary Schoola 0.026
(0.077)
Rural Electrificationa -0.115
(-1.457)
Roada 0.821
(4.584)*
Year -0.011
(-4.873)*
Year x Cool Months 0.002
(3. 835)*
Year x Length of 0.002
Rainy season (4.514)*
Year x Flood Potential -0.001
(-3.983)*
Year x Irrigation 0.0001
Potential (6.639)*
Year x Soil Moisture -0.0001
Capacity (-0.093)
Year x Excess Rain Months 0.005
(11.372)*.
No. of Cool Months 0.016
(1.156)
Length of Rainy Season 0.046
(2.735)*
Flood Potential -0.010
(-1;992)*
Irrigation Potential 0.002
(3.848)*
Soil Moisture Capacity 0.0002
(0.068)
Excess Rain Months 0.050
(2.985)*
Urban Distancea -0.205
(-0.089)
Constant -0.132
(-1.570)
F-Statistic 6.90 94.792
Hausman-Wu (Chi-square, 12) 51.631
NOTE: t-Statistics are in parenthesis. Asterisk refers to significance
level of 10 percent or better.
a Coefficients of these variables are in elasticity form.
- 24 -
regression clearly shows that Banks are more likely to locate in areas
where the road infrastructure and the marketing system are improving.
Markets provide both higher incomes to producers and reduce the price risk
they face, i.e. they improve their repayment capacity. And roads have an
eifect on farmer income, demand for inputs and hence credit and they reduce
the credit transactions costs of both the customers and the banks. Of the
two variables, roads have the most powerful effect with an elasticity of
about 0.83, followed by regulated markets with an elasticity of 0.20.
Markets, of course, are a relatively cheap investment which can be
increased much more rapidly than roads. Rural electrification does not
contribute to Bank gro-th. Indeed it has a negative effect which is
statistically significant at the 10 percent level.
The time trend and the interaction effects with time confirm that
Bank growth has been more rapid in districts with a high irrigation
potential, where the rainy season is longer, and where cool months allow
for the growth of wheat. This is fully consistent with the hypothesis that
banks have systematically located in environments which were favorable to
the green revolution technologies; i.e. that banks responded to
opportunities.-created by technical change. In addition bank growth was
lower where the flood potentially high, i.e. where they face higher risk
and where green revolution technology is less applicable because of lack of
water control.
VI. Determinants of Private Investment
The investment data in table 5 relate to average annual levels of
investment for each of the intercensus intervals for which we have data.
- 25 -
Table S. Effects of Aaroclimatic Endowments. Government Infrastructuro.
Commercial Bank *nd Prices on Agricultural Investment
(No. of Observations u 304)
Investment In
Explanatory Draft Milk
variable animals animals Small stocks Pumos Tractors
Aggregote real interno- 2.098 1.007 1.697 -0.497 -0.076
tional price, logged a (3.709)* (2.368)* (2.163)* (-1.327) (-0.197)
Real fertilizer price5 -12.262 -8.396 -7.836 -1.140 -1.127
(-4.292)* (-4.139)* (-2.662)* (-0.834) (-0.799)
Real urban wag, a 6.866 3.406 2.106 -0.470 1.127
(6.306). (5.684S) (1.977). (-0.922) (2.284).
Real interost rate -0.686 -6.115 -0.302 -0.109 0.092
(.3.688). (-1.163) (-1.691)§ (-1.162) (6.962)
Roads a -2.128 -1.443 1.186 0.107 -0.319
(-3.228). (-4.002)* (1.808)- (0.362) (0.983)
Canal irrigation a -0.198 -0.074 0.756 -0.067 0.481
(-0.327) (-0.220) (1.238) (-0.260) (1.616)§
Primary schools * .3.815 0.686 -0.949 -0.782 0.037
(2.341). (6."65) (-0.696) (-1.233) (0.047)
Electrification a 9.713 0.526 -0.157 6.56S -0.031
(1.967). (2.634)* (40.428) (2.072). (-0.173)
Commercial banks a 0.636 0.849 0.657 0.375 0.143
(2.492). (7.146)* (3;046)* (3.6806) (1.310)§
Regulated-markets a -0.066 0.212 0.225 -0.041 D .16V.
(-6.184) (1.119) (0.649) (-0.246) (0.910)
Rainfall x 1O3 1.506 11.309 -8.241 0.287 0.042
(0.362) 'i-801)* (-1.871). (6.711) (0.867)
Past stock -0.183 -0.688 -0.139 -0.047 0.134
(-12.62S). (-2:099). (-12.656). (-4.983)* (10.704).
Year -1.740 -1.160 -0.773 0.104 0.002
(-3.199)* (-1.438) (-1.340) (1.412) (0.259)
Year x cool month* 0 083 -0.289 0.408 0.028 0.002
(0-.813) (-1.912)* (3.748)* (2.119). (1.486)
Year x rainy season 0.136 0.693 0.127 -0.001 -6.661
(1.112) (3.876)* (0.997) (-0.904) (40.749)
Yar x flood potential 0.0o3 06027 06081 0.062 0.0063
(0.088) (0.479) (2.002)* (0.473) (6.067)
Year x irrigation 6.084 0.04 -6.010 0.0001 6.661
potential (0.904) (0.606) (-2.266)* (6.262). (2.603).
Year x Soil moisture 0.134 -0.218 0.096 -0.000 0.001
capacity (1.116) (-1.269) (0.791) (-0.002) (0.078)
Year x excess rain 0.121 -0.684 -0.307 -0.014 -0.602
months (1.130) (-4.326). (-2.747). (-0.968) (-1.780)*
- 26 -
CONT'D
Investment In
Explanatory Draft Milk
variable animals animals Small stocks Pumps Tractors
Length of rainy seoason -4.362 -48.949 -10.126 -0.096 0.989
(-4.481) (-2.691)* (-0.984) (-0.083) (0.336)
Flood potential -0.781 -2.913 -5.104 -0.133 -0.001
(-0.218) (-0.6.9) (-1.814) (-0.326) (-0.017)
Irrigation potential -o.0a7 -0.211 -O.OU -0.916 -0.012
(4G.696) (-0.8U0) (-8.686) (-4.342) (-2.396)o
Soil moisture capacity -14.200 16.706 -14.375 0.283 0.602
(-1.376) (6.689) (-1.277) (0.239) (0.613)
Excess rain months -4.644 48.692 16.868 0.707 0.156
(-0.484) (3.352)* (1.620)o (0.636) (1.367)
Urban distance a -0.086 -0.030 -0.062 -0.004 -6.6663
(-0.840) (-0.459) (-1.321) (-1.243) (-0.528)
No. of cool months 1.652 25.946 -21.461 -2.007 -0.113
(V.194) (2.028)* (-2.321)* (-:2.024). (-1.101)
Constant 178.718 121.793 198.410 -1.450 -0.632
(8.819). (1.489) (3.861). (-0.229) (-0.05)
F-Statistic 16.188 20.951 8.979 4.282 21.699
Hausman-Wu
(Chi-square'. 19 df) 20.808 16.420 21.161 10.574 11.169
Notes: t-Statistices are in parenthesis. Asterisk refers to significance
level of 10 percent or better on a two-tail test.
a Coefficients of these variables *re In elasticity form.
§ refers to significance level of 10 percent or better on a *ingle-tail test.
.
- 27 -
The independent variables similarly are the means for the intercensal
periods of the corresponding data. Unlike in the banking equation wiiich
used annual data and where fixed effects model were appropriate, the rardom
effects model is not rejected for the investment equations which used four
census data.
The lagged price of aggregate crop outputt (instrumented by the
aggregate international price index) has a positive effect on three of the
classes of investment (draft animals, milk animals and small stocks). For
the animals the elasticities of investment with respect to price are
between 1.0 and 2.9, i.e. much higher than typical values of output supply
elasticities for individual crops. But note that these elasticities are
investment elasticities and investment can be postponed and is therefore
much more volatile than current planned output. The elasticities of the
investment equations can therefore not be .directly compared to output
supply elasticities. An increase in the price of fertilizer reduces
private investment of all categories although the effect is not
statistically significant for pumps and tractors. If an investment good,
for example, draft animals were a substitute for fertilizer we should see a
positive substitution effect. But an increase in fertilizer price also
means reduced farm profits-and perhaps reduced liquidity, resulting in a
negative effect on investment. The negative fertilizer price elasticities
of the investments suggest that the negative profit or liquidity effects
dominate the positive substitution effect of fertilizer price increases.
If the urban wage measures the opportunity cost of labor in
agriculture, then its increase means both a positive cross-price effect (if
labor and capital goods are substitutes in production) and a negative
_ 28 -
profitability effect on farm capital investment. Unlike for the fertilizer
price, the positive substitution effects appear to dominate the negative
profit effects.13 The results suggest that a 10 percent increase in urban
wages would increase annual investment (not capital stocks) in draft
animals by about 57 percent, and in tractors by about 11 percent, milk
animals by about 34 percent, and small stocks by 21 percent. Rising
interest rates have negative effects on draft animals, milk animals, small
stocks and pump investments, but only the effects for draft animals and
small stock are statistically significant. Overall the price-and interest
variables are seen to influence investment decisions in the expected
direction.
Of the infrastructure variables the expansion of the commercial
bank branches appears to most clearly accelerate the pace of private
agricultural investment. A 10 percent increase in the number of cowmercial
bank branches increases investment in animals and pumpsets by between 4 to
8 percent. The effects on tractors is 1.4 percent. These are substtntial
effects of bank expansion on investments.
A 10 percent increase in electrified villages increases investment
into pumps (which are often driven by electricity) by 4 percent. Barnes
and Binswanger (1986), using fixed effects technique with Indian village
data, also found that electricity tended to increase the number of pumpsets
13/ The effects of urban wage may also capture the impact of an exogenous
increase in urban income on the demand for agricultural output. These
effects are expected to be positive which Chen only reinforce the
positive substitution effects.
_ 29 -
in the villages. The increase in electrification is also seen to spur
investment in draft and milk animals by 7 and 5 percent respectively.
These are effects of electrification which had not previously been
demonstrated.
Canal irrigation should not be expected to reduce any of the
investments, except for that pumpsets, which can be substituted for canal
irrigation. The results suggest that canal irrigation increases investment
in tractors with an elasticity of 0.48. The estimated positive effect on
small stocks investment is not statistically significant.
The effects of road expansion on investments are not very
convincing as roads appear to reduce investment in draft and milk animals
and increase investment in small stocks. This may partly be because the
growth data for roads is derived from state level statistics and does not
differ across the districts within a state. (However the level data for
roads for 1974 were available). Neither is it possible to show any effect
for reaulated markets. Primary school expansion increases draft animal
investment, while favorable rainfall within the census interval increases
only investment in milk animals.
The only comparable study using fixed effects techniques is the
BYBM study of tractor stocks which include a number of similar explanatory
variables. Unlike in the present study aggregate prices (both crop and
livestock) tend to increase tractor stocks with an elasticity of 0.16.
Fertilizer prices also reduced tractor stocks while urban wages increased
it (but not significantly). Again in contrast to the current results roads
and irrigation both had statistically significant positive effects on
tractor stocks.
- 30 -
The investment equations include the past stocks of the capital
item. Except for tractors, the higher the past stocks, the lower the
current investment, i.e., there is clearly stock adjustment. For tractors,
on the other hand, investment is much more rapid where past stocks are
larger. Tractor stocks were very small in India in 1961, with an average
of only 0.14 tractors per 10 sq. km. By 1982 this number had risen to
2.58. Unlike for the other capital items the tractor equation includes
both technology adoption and investment phenomena. The adoption process
has not-yet run its full courze. It is therefore not surprising to see
aistricts with an early lead in tractor stocks experiencing higher rate of
adoption-cum-investment, as an early lead may indicate better development
of the supporting sales and service infrastructure which is not measured.
The investment equations include the agroclimatic characteristics
themselves and their interaction with time. Together with the time trend
the interaction terms form the district-specific time trends. From the
interaction terms we can therefore see that investment in all items except
milk animals grew more rapidly in areas with more cool months, consistent
with a strong response of private investment to the green revolution in
wheat.
Tractors investment was less in areas with excess rain but more
rapidly in districts with a high irrigation potential, i.e. areas where on
account of double cropping demand for tractors is particularly high. Milk
animal investment was higher where the rainy season was longer while
smallstock, i.e. sheep and goats grew less in areas with high moisture
either from irrigation or from excess rain.
- 31 -
The pure effects of agroclimate variables themselves are their net
impacts other than via their impact on growth of infrastructure, changes in
prices or new technology. If investment tends to eventually bring capital
stocks into equilibrium with respect to agricultural opportunities, net
investment should become zero in the absence of other changes and no effect
of the agroclimatic factors should show up. This tendency can indeed be
seen in the resulting coefficients: only 7 out of a total of 35 are
statistically significant. This compares to 10 statistically significant
effects of the interaction terms with time (out of the same total of 35).
Since the expected sign of the agroclimatic variables in the investment
equations is zero, it is not worth interpreting them.
VII. Determinants of Fertilizer Demand and Aggregate Crop Output
For fertilizer demand and output supply the fixed effects model
has to be used. The measured permanent district characteristics are
therefore not sufficient to fully characterize the endowment of a district.
Fertilizer demand is seen to decline significantly when the price of
fertilizers is increased (elasticity of -0.57) and to increase when the
urban wage rises (elasticity of 0.13). This effect is statistically
significant if the appropriate one-tail test is used, as it makes no sense
to expect a negative sign here. In contrast, we see a perverse positive
interest elasticity, but the effect is.not statistically significant.14 A
positive interest rate effect may indicate that there is still an
14/ Since a positive sign expectation makes no-economic sense in this case
a one-tail test is inappropriate.
- 32 -
unresolved simultaneity problem with respect to this variable if government
responds to higher demand for fertilizers by increasing the rate of
interest. Fertilhzer demand increases with all infrastructure investments,
although effect of canal irrigation is not significant. The effects of
primary schools, regulated markets and commercial banks are particularly
large and precisely estimated.
These extremely clear results accord fully with what is known fromi
other studies of the growth of fertilizers. To the previous literature
they add the first estimates of the impact on fertilizer demand of changes
in the rural bank network.
Finally the agroclimate x time interactions accord -very well with
what what is known about the influence of agroclimatic endowments on the
potential of green revolution technologies, with fertilizer demand growing
especially fast in green revolution areas with cool months, irrigation or
high soil moisture capacity, while being held back in areas of excessively
high rainfall and poor water-control.
In order to illustrate the endogeneity problem of using domestic
prices in explaining aggregate crop output we present results with both the
domestic and the international price indices. The comparisons clearly show
that an endogeneity problem is being circumvented when prices are
instrumented via the international price indeX. The coefficient of the
domestic price is only 0.06 while that with the international price is
0.24. Moreover, the aggregate crop supply elasticity of 0.13 estimated
with the international price exceeds the elasticities estimated for India
in BBQ and that estimated for the world in BYBM by a factor of 1.5 to 2.
- 33 -
Table Fixed Effects of covornmont Infrastructure. Commercial Bank and Prices
on Fertilizer Demand and Aggregato Crop Output
Aspreast. CrOp Output
Explanatory Variable Fertilizer Domestic Prieos International Prices
Demand
Aggregate roal price index 0.068 9.046 0.130
(lagged) a (1.649) (1.884)§ (6.472)*
Real fertilizer price a -0.572 0.021 -0.117
(-3.484)o (0.486) (-2.316)o
Roal urban wage a 0.125 0.056 0.063
(1.424)§ (1.548)§ (1.491)§
Real interest rate 0.025 0.004 * -O.W1
(1.308) (0.642) (-0.202)
Road a 0.224 0.216 0.201
* ~(1.789). (6.968). (6.549).
Canal irrigation 0.069 0.03a 0.026
(0.638) (1.039) (0.827)
Primary school a 1.433 * .881 0.835
(6.291)* (4.216)- (4.322)*
Rural electrification a 0.0865 .031 0.028
(1.889)§ (1.746). (1.693)§
Comercial bank a 0.247 0.018 0.020-
(c.6e7)0 (1.766). (1.918)*
Regulated "rket * 0.406 0.079 0.O84
(6.687). (4.627). (4.972)*
Rainfall x 193 1.273 0.073 0.071
(1.252) (3.482). (3.468)*
Year -2.236 -0.019 -0.026
(-6.358). (-3.179)* (-4.299)*
Year x cool months 0.207 0.008 0.090
(2.998). (4.891)* (4.816)*.
Year x length rainy season -6.097 -.003 -9.99
(-9.976) (-2. 4).* (-1.989).*
Year x flood potential -0.084 -0. D1 -0.001
(-1.247) (-3.716)* (-3.679).
Year x irrigation potential 0.023 0.001 0.001
(6.781)* (12.440)e (12.OS7)*
Year x soil moisture capacity 0.57a 0.006 0.006
(6.600). (4.324). (3.791).
Yoer x excose rain months -0.398 -.W05 -0.W04
(-4.306)s (-3.649)* (-3.086)*
F-Statistic 79.967 98.864 103.936
Hausman-Wu 42.918 46.308 44.764
(Chi-square, 18)
No. of Obsorvation 1148 1785 1786
Notes: t-statistice are in parenthesis. Asterisk r0fore to significance
level of 10 percont or better on a two-tail test.
a coefficients are in elasticity form.
§ refors to a 10 percent level of significanco on a onw-tail test.
- 34 _
Nevertheless, the classic result of the aggregate supply elasticity
literature is reconfirmed: short-run aggregate crop supply elasticities are
small and do not exceed a level of 0.2.13
The endogeneity problem with the output price also affects the
estimation of the fertilizer price elasticity. Using the domestic output
price the fertilizer price does not appear to affect aggregate output. In
the specification using international output prices the fertilizer price
elasticity is negative (-.12) and statistically significant. The results
are consistent with BYBM (1986) and BBQ (1984). The remaining discussion
will therefore only consider the results with the international price.
Increasing real interest rates of the cooperative sector reduces
aggregate output marginally an elasticity of about -0.001, while increasing
the density of commercial banks tends to increase crop output with an
elasticity of 0.020. The expansion of financial institutions thus seems to
exert a direct impact on aggregate crop output and a larger effect in
fertilizer demand as. well as on some of the private agricultural
investments.
Except for irrigation, all other infrastructure variables affect
aggregate crop output positively. The overwhelming impact of
infrastructure on aggregate crop output found in BYBM for international
13/ We attempted to estimate the long-run aggregate elasticity of supply
with this data set, using a free form lag structure of five past
prices. The sum of the coefficients was .28, and we could not, with
this technique, estimate a larger long-run aggregate supply response.
However, long-run supply responses must be-larger, as discussed in
BYBM.
- 35 _
data is thus confirmed in this Indian study. Quantitatively the effects o-
primary schools and roads are the largest, with elasticities of 0.34 and
0.20 respectively. BYBM found large effects for education as well. They
also found large irrigation effects but irrigation in the BYBM study
include both private and canal irrigation.
As expected residual growth of output (after account is taken of
prices, interest rates, infrastructure and banks) was larger in areas with
good irrigation potential, high soil moisture capacity, and cool winters.
It was lower in areas with excess rain and in areas liable to flooding.
VIII. Discussion
In the paper we have successfully demonstrated that with
appropriate panel data it is possible to overcome simultaneity and
unobservable variable problems arising from the joint dependence of the
decision of farmers, financial institutions and government agencies-on
location and agroclimatic factors of the region within they operate. It
then becomes possible to explain in an integrated fashion how the decision
of these actors interact and ultimately affect agricultural investment and
output. In addition, by judiciously using international prices we have
shown that it is possible to overcome simultaneity problems which have long
plagued the analysis of aggrogate supply response to output prices.
The reduced form regressions of infrastructure, banks, investments
and output on agroclimatic and location characteristics show the
overwhelming importance these factors which must have had over the history
of these districts on all decision-makers in the system. The importance of
- 36 -
the interaction terms with time shows that the agroclimate factors have
continued to govern the rate at which districts can take advantage of new
agricultural opportunities and have continued to govern public, bank and
private investuent allocation decisions over the period analyzed.
For the first time this paper presents results on the effect of
the expansion of financial intermediation on agricultural investment and
output .which are not seriously flawed because they ignore fungibility of
financial resources or the other econometric problems discussed above. The
expansion'of the commercial banks into rural areas had a large'effect on
fertilizer consumption and on fixed private investment. It also affects
output, but with an elasticity of only 0.02. In order to see how much the
bank expansion has contributed we tabulate in table 7 the estimated impact
of all independent variable, on the dependent variables in the decade of
the'1970s. These estimates are the percentage change in the dependent
variable caused by the changes in the independent variable, estimated as
the product of the change in the independent variable times the regression
coefficient which is divided by the average value of the dependent
veriable. Here we can see the contributions of different factors to growth
of dependent variables over the decade, 1971-1981. Obviously the effects
of a particular variable will be small if it did not change much over the
decade, irrespective of its potential impact as measured by the regression
coefficient.
The rapid Bank expansion increased fertilizer demand by about 23
percent, investment levels in tractors by 13 percent, investment in pumps
by 41 percent, milk animals by 46 percent, and in draft animals by about 38
percent. They also increased the aggregate crop output by nearly 3
- 37 -
percent. This contribution to output is less than that of any other
infrastructure variable except canal irrigation and rural electrification.
Given the large contribution of the banks to investment and fertilizer use,
their impact on output appears to be fairly small. It may arise because,
while spurring specific investments on account of their lending activity,
the banks also may reduce liquidity in rural areas by their transfer of
rural deposits to urban areas. This issue will be investigated in a future
paper.
TABLE 7. Contrit!,t.ions of Difforent Factor. to Growth of Dependent Variables 1971-1991
Variable Output Fertillzer Pump Tractor Draft Milk
Animal Animal
Aggregat. real price index 0.023* e.o25 -9.026 -.008 6.161* o.084*
Real price of frtilIlzer -0.009* -.044* -0.02 -0.021 -0.174* -0.092.
Real urban wage 0.009§ 0.018§ -0.117 0.254* 0.912* 0.558.
Interest rato 0.001 0.028 -0.018 0.01 -0.083* -0.012
Road 0.0687 0.06* 0.0.40 0.101 -6.513* -0.356*
Canal irrigation O.6Z4 O.68 -0.008 0.058§ -0.018 -0.007
Primary school 0.080* 0.304* -0.171 0.007 0.642. 0.086
Rural electrification 0.021* 0.048§ 0.281* -0.021 0.385S 0.271*
Commercial bank 0.626. 0.229.* 0.408. 0.131. 0.878* 0.610*
Regulated market 60.44* 0.17. -0.028 0.075 -0.024 0.079
Growth explainod by all factors 0.266 0.849 0.341 0.594 1.66 1.171
Actual growth 0.239 6.729 -0.081 1.327 1.924 1.664
Not.: Asterisk refers to significance level of 16 percent or better on a two-tail test.
§ refers to significance lovel of 10 percent on a one-tall teot.
In addition to estimating the impact of the banks we have also
shown that commercial banks prefer to locate in well-watered areas where
agricultural risks are relatively low and avoid areas characterized by high
risks of droughts and floods. Moreover, bank expansion is greatly
facilitated by government investments in roads-and regulated markets which
- 38 -
enhance the liquidity position of farmers and reduce transaction costs of
both bank and farmers.
Our estimates of interest elasticity of investment while
econometrically a bit less secure than the effect of bank expansion,
suggest that changes in real interest clearly reduce some of the long-term
private investments. On the other hand, we are unable to show an impact of
interest rates on either fertilizer demand or aggregate output, i.e. the
impact of higher interest rates on reducing investment in long-term assets
is not sufficiently large to have a perceptible impact on output. And for
short-term credit used to buy fertilizer it appears that availability of
credit (as measured by the bank network) is clearly more important than the
interest rate.
In the reduced form cross-sectional regressions regions with high
irrigation potential have larger population density, better infrastructure,
a more developed banking system, higher private investment rates and higher
aggregate crop output. They are also favored in the allocation of new
infrastructure and are preferred by banks. They are also able to banefit
more from new technology in terms of fertilizer demand, investments and-
output. On the other hand, the analysis of the government's own
additional investment in irrigation between 1961 and 1981 suggest positive,
but barely statistically significant, impact of these irrigation
investments on Bank expansion, tractor investment and crop output. As
table 7 shows the estimates imply a near zero contribution of canal
irrigation to aggregate crop output over the 1970s. In order to understand
this puzzle it is important to recall that the measure of irrigation
potential must include both the already developed as well as the yet to be
- 39 -
developed potential. There is therefore collinearity between past
investment in canal irrigation and potential and the reduced form effects
of irrigation potential include the effects of the past investments. The
fixed effects analysis over time, however, looks only at the impact of the
additional government investment over the period. Therefore finding a low
impact of recent government investments is not inconsistent with high
Impact of past investment. This would be especially the case if the rate
of return to new canal irrigation had been declining over time as the best
sites for irrigation became progressively exhausted. Moreover the estimates
do not measure the impact of the very important private investment in
irrigation.14 The findings therefore do not imply that private investment
in irrigation had a low return, an issue which cannot be analyzed with the
techniques utilized. Canal irrigation investment in the 1960s and 1970s
was insignificant compared to private investment. Over the 21 years
analyzed, area irrigated by the government (canal) increased from 58 ha to
75 ha per 1000 ha of geographic area. Area irrigated by wells, i.e.
privately increased much-more rapidly from 54 ha to 114 ha per 1000 ha of
geographic area, i.e. private additions to irrigation exceeded government
additions by a factor of nearly 4 to 1.
Improved road investment has been shown to enhance agricultural
output with an elasticity of about 0.20. In the 85 sample district roads
have on average increased by 40 percent between 1971 and 1981. Roads would
thus have contributed directly 7 percent each to the growth of agricultural
14/ These results are not altered if the canal irrigation variable is
replaced by the public irrigation, i.e. ineluding the area under tanks
which has been declining.
-40-
output and fertilizer use over this period. We have seen above that they
have also contributed to bank expansion. On the other hand, for given bank
density and other infrastructure, the direct effect of roads on private
investment is mixed, suggesting that tho major effect of roads is not via
their impact on private agricultural investment but rather on marketing
opportunities and reduced transaction costs of all sorts.
Regulated markets have an elasticity with respect to output of
0.08. They have expanded rapidly after 1969 and the growth (87 percent)
during this period would have contributed nearly 4 percent to agricultural
output and 17 percent to the demand of fertilizers. As in the case of
roads, the markets also have little effect on the private investments, i.e.
their effect works directly on output supply decisions.
In contrast electrification has a clear impact on inveatment in
fixed capital, especially on pumps where it has contributed an increase of
28 percent to investment levels. Via these investments and klso via
fertilizer demand (about 5 percent increase) electrification has increased
output over the decade by about 2 percent.
Finally primary education has added 8 percent to crop output over
the decade, a very large effect indeed. This has come about primarily via
a nearly 30 percent increment to fertilizer demand.
In terms of prices the study confirms that short run aggregate
crop supply elasticities are inelastic even once simultaneity problems
which have plagued this literature are overcome. In addition it shows that
output prices, fertilizer prices and urban wages can have substantial
- 41 -
impacts on private fixed capital investments even in the long run, as the
lagged prices in these equations refer to prices ruling in the previous
intercensal period, i.e. 5 years earlier on average. The results suggest
that wages increases tend to lead to increased private investment while
fertilizer price increases tend to reduce investments. Thus for wages
substitution effects dominate the profitability effects while for
fertilizers the opposite is the case.
The agricultural development literature has been dominated by
schools which tended to emphasize the importance, or lack thereof, of
specific determinants of agricultural growth. Price fundamentalists have
been at odds with irrigation determinists. The 1970s and early 1980s have
been dominated by advocates of cheap sources of growth from agricultural
research and education. In World Bank projects road infrastructure and
market development have taken a backseat relative to the forced expansion
of cheap agricultural credit. Advocates of such agricultural credit have
been attacked by scholars emphasizing the virtues of savings and market-
determined interest rates. As the evidence in this paper suggests, reality
is far too complex to be put into such black and white terms. Prices
really do matter but so do infrastructure, markets, and banks.
_ 42 -
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